Simulation of synthetic complex data: The R package simPop

Templ, Matthias; Meindl, Bernhard; Kowarik, Alexander; Dupriez, Olivier

doi:10.21256/zhaw-4237

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-4237

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Simulation of synthetic complex data: The R package simPop
Authors:	Templ, Matthias Meindl, Bernhard Kowarik, Alexander Dupriez, Olivier
DOI:	10.21256/zhaw-4237 10.18637/jss.v079.i10
Published in:	Journal of Statistical Software
Volume(Issue):	79
Issue:	10
Page(s):	1
Pages to:	38
Issue Date:	2017
Publisher / Ed. Institution:	UCLA, Dept. of Statistics
ISSN:	1548-7660
Language:	English
Subjects:	Microdata; Simulation; Synthetic data; Population data; R
Subject (DDC):	005: Computer programming, programs and data 510: Mathematics
Abstract:	The production of synthetic datasets has been proposed as a statistical disclosure control solution to generate public use files out of protected data, and as a tool to create "augmented datasets" to serve as input for micro-simulation models. Synthetic data have become an important instrument for ex-ante assessments of policy impact. The performance and acceptability of such a tool relies heavily on the quality of the synthetic populations, i.e., on the statistical similarity between the synthetic and the true population of interest. Multiple approaches and tools have been developed to generate synthetic data. These approaches can be categorized into three main groups: synthetic reconstruction, combinatorial optimization, and model-based generation. We provide in this paper a brief overview of these approaches, and introduce simPop, an open source data synthesizer. simPop is a user-friendly R package based on a modular object-oriented concept. It provides a highly optimized S4 class implementation of various methods, including calibration by iterative proportional fitting and simulated annealing, and modeling or data fusion by logistic regression. We demonstrate the use of simPop by creating a synthetic population of Austria, and report on the utility of the resulting data. We conclude with suggestions for further development of the package.
URI:	https://digitalcollection.zhaw.ch/handle/11475/5698
Fulltext version:	Published version
License (according to publishing contract):	CC BY 3.0: Attribution 3.0 Unported
Departement:	School of Engineering
Organisational Unit:	Institute of Data Analysis and Process Design (IDP)
Appears in collections:	Publikationen School of Engineering

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Templ, M., Meindl, B., Kowarik, A., & Dupriez, O. (2017). Simulation of synthetic complex data: The R package simPop. Journal of Statistical Software, 79(10), 1–38. https://doi.org/10.21256/zhaw-4237

Templ, M. et al. (2017) ‘Simulation of synthetic complex data: The R package simPop’, Journal of Statistical Software, 79(10), pp. 1–38. Available at: https://doi.org/10.21256/zhaw-4237.

M. Templ, B. Meindl, A. Kowarik, and O. Dupriez, “Simulation of synthetic complex data: The R package simPop,” Journal of Statistical Software, vol. 79, no. 10, pp. 1–38, 2017, doi: 10.21256/zhaw-4237.

TEMPL, Matthias, Bernhard MEINDL, Alexander KOWARIK und Olivier DUPRIEZ, 2017. Simulation of synthetic complex data: The R package simPop. Journal of Statistical Software. 2017. Bd. 79, Nr. 10, S. 1–38. DOI 10.21256/zhaw-4237

Templ, Matthias, Bernhard Meindl, Alexander Kowarik, and Olivier Dupriez. 2017. “Simulation of Synthetic Complex Data: The R Package simPop.” Journal of Statistical Software 79 (10): 1–38. https://doi.org/10.21256/zhaw-4237.

Templ, Matthias, et al. “Simulation of Synthetic Complex Data: The R Package simPop.” Journal of Statistical Software, vol. 79, no. 10, 2017, pp. 1–38, https://doi.org/10.21256/zhaw-4237.